Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

/pdf/measuring-user-influence-in-twitter-the-million-follower-44v20fo7ym.pdf

Measuring User Influence in Twitter: The Million Follower Fallacy

Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician Our results show that Twitter is indeed used extensively for political deliberation We find that the mere number of messages mentioning a party reflects the election result Moreover, joint mentions of two parties are in line with real world political ties and coalitions An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research

/pdf/predicting-elections-with-twitter-what-140-characters-reveal-5bagzv2skv.pdf

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of 'culturomics,' focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.

/pdf/quantitative-analysis-of-culture-using-millions-of-digitized-1p7ygzqdx7.pdf

Quantitative analysis of culture using millions of digitized books

In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?

/pdf/the-parable-of-google-flu-traps-in-big-data-analysis-52z3z6f7bu.pdf

The Parable of Google Flu: Traps in Big Data Analysis

This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.

/pdf/detecting-influenza-epidemics-using-search-engine-query-data-3do2njl33s.pdf

Detecting influenza epidemics using search engine query data

Background 
Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.


Methodology/Principal Findings 
We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.


Conclusions 
Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.

/pdf/assessing-google-flu-trends-performance-in-the-united-states-45a42w3zbv.pdf

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

We describe a walking microrobot that is propelled by cilialike thermal bimorph actuator arrays. The robot consists of two actuator array chips, each having an 8 × 8 array of “motion pixels,” which are composed of four orthogonally oriented cilia. Each group of unidirectional cilia is controlled independently for each chip, which provides planar motion with three degrees of freedom (x, y, θ). The robot is approximately 3 cm in length, 1 cm in width, and 0.9 mm in height and has a mass of 0.5 g. By varying the actuation frequency and motion gait strategy, the direction and velocity of the motion can be controlled. In this paper, we present the system architecture, control mechanism, and modeling of the robot, as well as experimental results, during linear and rotary motion. The robot can carry loads up to seven times its own mass, and it can operate at speeds up to 250 μm/s with step sizes from 1 to 4 μm.

Thermally Actuated Omnidirectional Walking Microrobot

An omnidirectional mobile microrobot realized by microelectromechanical system (MEMS) actuator arrays is presented. The microrobot consists of two rigidly connected microcilia array chips, each having an 8 8 array of “motion pixels,” which are composed of four orthogonally oriented thermal bimorph actuators. This allows for reliable, accurate motion in three degrees of freedom x y θ in the plane, a first for a microrobot of this kind. The microrobot is approximately 3cm in length, 1cm in width, 1mm in height, and has a mass of less than half a gram. By varying the input power, actuation frequency and motion gait strategy the velocity of the chip can be precisely controlled. Motion in three degrees of freedom has been demonstrated and a maximum velocity of 635 µm/s and carrying capacity greater than 1.448 g (two 8-pin ICs) has been observed. The microrobot has been characterized extensively and a model for its performance is described.

Omnidirectional walking microrobot realized by thermal microactuator arrays

Re: Breyer et al.: Use of Google Insights for Search to Track Seasonal and Geographic Kidney Stone Incidence in the United States (Urology 2011;78:267-271)

Matthew H. Mohebbi

Papers

Detecting influenza epidemics using search engine query data

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Thermally Actuated Omnidirectional Walking Microrobot

Omnidirectional walking microrobot realized by thermal microactuator arrays

Re: Breyer et al.: Use of Google Insights for Search to Track Seasonal and Geographic Kidney Stone Incidence in the United States (Urology 2011;78:267-271)